scholarly journals Internal evaluation criteria for categorical data in hierarchical clustering

2018 ◽  
Vol 15 (2) ◽  
Author(s):  
Zdeněk Šulc ◽  
Jana Cibulková ◽  
Jiří Procházka ◽  
Hana Řezanková
Keyword(s):  
Hierarchical Clustering ◽  
Categorical Data ◽  
Likelihood Function ◽  
Evaluation Criteria ◽  
Optimal Number ◽  
Number Of Clusters ◽  
Internal Evaluation ◽  
Correct Number ◽  
Cluster Quality ◽  
Optimal Number Of Clusters

The paper compares 11 internal evaluation criteria for hierarchical clustering of categorical data regarding a correct number of clusters determination. The criteria are divided into three groups based on a way of treating the cluster quality. The variability-based criteria use the within-cluster variability, the likelihood-based criteria maximize the likelihood function, and the distance-based criteria use distances within and between clusters. The aim is to determine which evaluation criteria perform well and under what conditions. Different analysis settings, such as the used method of hierarchical clustering, and various dataset properties, such as the number of variables or the minimal between-cluster distances, are examined. The experiment is conducted on 810 generated datasets, where the evaluation criteria are assessed regarding the optimal number of clusters determination and mean absolute errors. The results indicate that the likelihood-based BIC1 and variability-based BK criteria perform relatively well in determining the optimal number of clusters and that some criteria, usually the distance-based ones, should be avoided.

scholarly journals A Hybrid Clustering Approach for Bag-of-Words Image Categorization

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Hui Huang ◽  
Yan Ma
Keyword(s):  
Hierarchical Clustering ◽  
Optimal Number ◽  
Bag Of Words ◽  
Image Categorization ◽  
Vocabulary Size ◽  
Number Of Clusters ◽  
Visual Words ◽  
Initial Cluster ◽  
Clustering Approach ◽  
Optimal Number Of Clusters

The Bag-of-Words (BoW) model is a well-known image categorization technique. However, in conventional BoW, neither the vocabulary size nor the visual words can be determined automatically. To overcome these problems, a hybrid clustering approach that combines improved hierarchical clustering with a K-means algorithm is proposed. We present a cluster validity index for the hierarchical clustering algorithm to adaptively determine when the algorithm should terminate and the optimal number of clusters. Furthermore, we improve the max-min distance method to optimize the initial cluster centers. The optimal number of clusters and initial cluster centers are fed into K-means, and finally the vocabulary size and visual words are obtained. The proposed approach is extensively evaluated on two visual datasets. The experimental results show that the proposed method outperforms the conventional BoW model in terms of categorization and demonstrate the feasibility and effectiveness of our approach.

scholarly journals PENERAPAN k-MODES CLUSTERING DENGAN VALIDASI DUNN INDEX PADA PENGELOMPOKAN KARAKTERISTIK CALON TKI MENGGUNAKAN R-GUI

2021 ◽  
Vol 10 (3) ◽  
pp. 359-366
Author(s):  
Hanik Malikhatin ◽  
Agus Rusgiyono ◽  
Di Asih I Maruddani
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Categorical Data ◽  
Optimal Number ◽  
Class I ◽  
Number Of Clusters ◽  
The Government ◽  
Index Value ◽  
Cluster 2 ◽  
Optimal Number Of Clusters

Prospective TKI workers who apply for passports at the Immigration Office Class I Non TPI Pati have countries destinations and choose different PPTKIS agencies. Therefore, the grouping of characteristics prospective TKI needed so that can be used as a reference for the government in an effort to improve the protection of TKI in destination countries and carry out stricter supervision of PPTKIS who manage TKI. The purpose of this research is to classify the characteristics of prospective TKI workers with the optimal number of clusters. The method used is k-Modes Clustering with values of k = 2, 3, 4, and 5. This method can agglomerate categorical data. The optimal number of clusters can be determined using the Dunn Index. For grouping data easily, then compiled a Graphical User Interface (GUI) based application with RStudio. Based on the analysis, the optimal number of clusters is two clusters with a Dunn Index value of 0,4. Cluster 1 consists of mostly male TKI workers (51,04%), aged ≥ 20 years old (91,93%), with the destination Malaysia country (47%), and choosing PPTKIS Surya Jaya Utama Abadi (37,51%), while cluster 2, mostly of male TKI workers (94,10%), aged ≥ 20 years old (82,31%), with the destination Korea Selatan country (77,95%), and choosing PPTKIS BNP2TKI (99,78%). 

scholarly journals A genetic algorithm using Calinski-Harabasz index for automatic clustering problem

2020 ◽  
Vol 12 (3) ◽  
pp. 97-106
Author(s):  
Suzane Pereira Lima ◽  
Marcelo Dib Cruz
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Cluster Validity ◽  
Number Of Clusters ◽  
Correct Number ◽  
Clustering Problem ◽  
Automatic Clustering ◽  
Optimal Number Of Clusters

Data clustering is a technique that aims to represent a dataset in clusters according to their similarities. In clustering algorithms, it is usually assumed that the number of clusters is known. Unfortunately, the optimal number of clusters is unknown for many applications. This kind of problem is called Automatic Clustering. There are several cluster validity indexes for evaluating solutions, it is known that the quality of a result is influenced by the chosen function. From this, a genetic algorithm is described in this article for the resolution of the automatic clustering using the Calinski-Harabasz Index as a form of evaluation. Comparisons of the results with other algorithms in the literature are also presented. In a first analysis, fitness values equivalent or higher are found in at least 58% of cases for each comparison. Our algorithm can also find the correct number of clusters or close values in 33 cases out of 48. In another comparison, some fitness values are lower, even with the correct number of clusters, but graphically the partitioning are adequate. Thus, it is observed that our proposal is justified and improvements can be studied for cases where the correct number of clusters is not found.

Clustering Count-based RNA Methylation Data Using a Nonparametric Generative Model

2018 ◽  
Vol 14 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Lin Zhang ◽  
Yanling He ◽  
Huaizhi Wang ◽  
Hui Liu ◽  
Yufei Huang ◽  
...  
Keyword(s):  
Clustering Analysis ◽  
Methylation Level ◽  
Optimal Number ◽  
Generative Model ◽  
Methylation Data ◽  
Sequencing Data ◽  
Number Of Clusters ◽  
Rna Methylation ◽  
Clustering Effect ◽  
Optimal Number Of Clusters

Background: RNA methylome has been discovered as an important layer of gene regulation and can be profiled directly with count-based measurements from high-throughput sequencing data. Although the detailed regulatory circuit of the epitranscriptome remains uncharted, clustering effect in methylation status among different RNA methylation sites can be identified from transcriptome-wide RNA methylation profiles and may reflect the epitranscriptomic regulation. Count-based RNA methylation sequencing data has unique features, such as low reads coverage, which calls for novel clustering approaches. <P><P> Objective: Besides the low reads coverage, it is also necessary to keep the integer property to approach clustering analysis of count-based RNA methylation sequencing data. <P><P> Method: We proposed a nonparametric generative model together with its Gibbs sampling solution for clustering analysis. The proposed approach implements a beta-binomial mixture model to capture the clustering effect in methylation level with the original count-based measurements rather than an estimated continuous methylation level. Besides, it adopts a nonparametric Dirichlet process to automatically determine an optimal number of clusters so as to avoid the common model selection problem in clustering analysis. <P><P> Results: When tested on the simulated system, the method demonstrated improved clustering performance over hierarchical clustering, K-means, MClust, NMF and EMclust. It also revealed on real dataset two novel RNA N6-methyladenosine (m6A) co-methylation patterns that may be induced directly by METTL14 and WTAP, which are two known regulatory components of the RNA m6A methyltransferase complex. <P><P> Conclusion: Our proposed DPBBM method not only properly handles the count-based measurements of RNA methylation data from sites of very low reads coverage, but also learns an optimal number of clusters adaptively from the data analyzed. <P><P> Availability: The source code and documents of DPBBM R package are freely available through the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/DPBBM/.

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